A Mid-Level Feature Fusion Framework Integrating PHLF and VGG16 for Robust Batik Pattern Detection

Javanese batik is characterized by distinctive motif patterns. The rapid evolution of designs through motif combination has introduced increased complexity, posing challenges for the community. People are increasingly less familiar with traditional Javanese original batik patterns. Many studies using CNN have been conducted to recognize batik patterns. However, it is important to improve object detection performance by strengthening explicit local features. To answer this question, our study aims to improve the detection of batik patterns that have local texture patterns and complex orientations that are in harmony with batik geometry with an integrated fusion scheme. To improve detection, an integrated fusion schema model was developed using the PHLF hybrid framework as a geometrically oriented local feature with VGG16 as a deep feature extractor for object detection. According to research, although VGG16 is reliable on large benchmarks such as ImageNet, VGG16 is less reliable for subtle intra-motif variations, which can suppress accuracy. Evidence from the results of the study shows that in the eight-class dataset consisting of 6,400 images for training data and 1.600 images for test data, a mid-level feature fusion approach on VGG16 with integrated PHLF improves resistance to data variations such as lighting, fabric deformation, and background complexity. The experimental results showed that the model achieved an mAP value of 0.68 at IoU = 0.5 and 0.2846 at IoU = 0.7. The significant difference between mAP@0.5 and mAP@0.7 suggests that the model still has limitations in the precision of the localization of the boundary box.